Domain Adaptation with Topical Correspondence Learning

A serious and ubiquitous issue in machine learning is the lack ofsufficient training data in a domain of interest. Domain adaptation isan effective approach to dealing with this problem by transferringinformation or models learned from related, albeit distinct, domains tothe target domain. We develop a novel domain adaptation method fortext document classification under the framework of Non-negativeMatrix Factorization. Two key ideas of our method are to construct alatent topic space where a topic is decomposed into common wordsshared by all domains and words specific to individual domains, andthen to establish associations between words in different domainsthrough the common words as a bridge for knowledge transfer. Thecorrespondence between cross-domain topics leads to more coherentdistributions of source and target domains in the new representationwhile preserving the predictive power. Our new method outperformedseveral state-of-the-art domain adaptation methods on severalbenchmark datasets.